Senior Machine Learning Engineer

This role will be based in Dublin, Ireland. 

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

We are seeking a Senior Machine Learning Engineer for the development of advanced LLM-powered conversational agents and the evaluation frameworks that ensure their effectiveness, relevance, and groundedness. These systems will power next-generation agentic experiences across LinkedIn, enabling professionals and organizations to connect more meaningfully.

In this role, you will help shape long-term architecture and strategy for agentic AI. You’ll apply the latest advancements in Generative AI, large language models, multi-agent frameworks to build scalable systems that deliver measurable value to LinkedIn members. You will also design and implement rigorous evaluation methodologies, covering relevance, quality, and groundedness to ensure LinkedIn’s conversational agents meet the highest standards and drive adoption across members.

You will drive LinkedIn's efforts in cutting-edge AI research and development, focusing on large-scale foundation models. In this role, you will specialize in developing, training, and optimizing foundation models, including Large Language Models (LLMs), utilizing state-of-the-art deep learning architectures such as multi-billion parameter transformers and scaling them to serve LinkedIn's global user base. Your work will emphasize the design and deployment of high-performance AI systems, leveraging GPUs and other hardware accelerators for large-scale training and inference. You will collaborate with a world-class team of researchers and engineers to develop and advance the next generation of AI models to push the boundaries of AI at scale, working on complex problems such as distributed training, model parallelism, and system co-design. A successful candidate will be comfortable operating at the intersection of AI research and engineering, with a deep understanding of the latest advancements including but not limited to large model training, post-training techniques for planning and reasoning, fine-tuning strategies, reinforcement learning techniques.

Responsibilities:

• Research, design, and development of large-scale foundation AI models, with a focus on LLMs, transformer architectures.

• Publish the findings and innovations as the latest academic and industry research in top notch Core AI venues such as ICML, NeurIPS, ICLR, ACL.

• Drive innovation in large-scale model training, leveraging GPUs and specialized hardware for optimal performance.

• Develop and maintain scalable AI pipelines, optimizing training and inference workflows to handle vast amounts of data.

• Collaborate with cross-functional teams of machine learning engineers, infrastructure engineers, and AI researchers to deliver impactful AI innovations across LinkedIn's products.

 

Basic Qualifications:

• BA/BS Degree in Computer Science, Machine Learning or related technical discipline, or related practical experience.

• 2+ years experience in machine learning or AI engineering.

• 2+ years experience in design and development of algorithmic solutions

• 2+ years experience with programming languages such as Java, Python, etc.

Preferred Qualifications:

• 4+ years of hands-on experience in large-scale model training, model-system co-design, or large AI systems.

• PhD in Computer Science, Machine Learning, or related technical discipline with a focus on advanced AI/ML techniques

• Proven experience in building and optimizing large-scale AI/ML models using frameworks such as PyTorch, PySpark, and CUDA.

• Several recent publications in top AI conferences on relevant areas including but not limited to Large Scale Model Training and LLM Algorithm Optimization, deep learning, reinforcement learning, foundation models etc.

• Expertise in distributed training, model parallelism, and hardware acceleration for AI workloads, including GPUs and TPUs.

• Demonstrated ability to solve complex AI challenges in large-scale environments, optimizing models for efficiency and scalability.

• Strong proficiency in AI system design, model optimization, and the development of production-quality AI pipelines.

• Published work in AI research or a significant contribution to industry-leading AI technologies.

Suggested Skills:

• Large Language Models (LLM)

• Generative AI (GAI)

• Agentic systems

• AI Research and Development

• GPU and Hardware Acceleration

• Distributed AI Systems and Model Parallelism

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